Chief among these desires for simplification is the ability to attribute success within an ad campaign. Ask a group of marketers what performed best in their most recent campaign and the group is likely to be split in two: those who don’t know and those who are wrong.

We’re not much closer to solving the attribution problem today than we were just a few years ago. For answers, I think we should look at an area that has indeed made significant progress: Amazon’s Alexa, which represents gigantic progress for the general advancement of natural language processing and artificial intelligence.

The success and progress in this field can serve as a lesson for marketers, and it’s not likely one that they’ve considered. Building the software and ecosystem behind Alexa was idiosyncratic, tedious and required true geniuses of our time to contribute to such a project. Just like attribution, Alexa needs to interpret intent, not just data.

Can you imagine if Amazon’s engineers started out project Alexa like a marketer? “Just tell us what to do. This is too hard.” Of course they would not have done that because the brilliant minds behind Alexa had data science, math, statistics and computer science degrees from some of the finest learning institutions on the planet.

In comparison, who is usually interpreting results from digital campaigns and deciding where to spend money? It varies. I have a journalism degree from a state school and I’m told I’m reasonably good at interpreting campaign results. Oh boy.

The Methbot fraud that gripped the industry at the end of last year reminded us yet again that our need to measure our advertising’s impact is often confused and thwarted by external factors. For example, most marketers still use “last-ad” (clicked or viewed) attribution as opposed to full path attribution. This means that when a fraudulent ad is the last ad viewed, machines will optimize to the sources of that fraud. But fraud is not our only problem plaguing last-ad attribution. Unviewable ads do the same, as do cookie-bombed impressions.

It’s puzzling why attribution, especially in our digital age of unprecedented data, is a topic that everyone agrees needs to be simplified given the immense complexity in our ecosystem.

Distinguishing signals from noise is a problem in any industry. Media’s challenge is that there are more misleading signals than accurate ones, even after the noise appears to be removed. Ad tech data scientists routinely tell me, “I’ve worked in a variety of industries and no industry’s data is as dirty and misleading as we find in ad tech.”

Is what we have here even more complex than the challenges that faced the Alexa developers in developing a natural language processing and AI ecosystem? I think making that argument is easier than we may think.

If it’s truly as complex as I’m suggesting, what is a marketer or agency media planner to do? Approach this problem with the understanding that it’s likely no easier than building an AI ecosystem like Alexa. Solving for human behavior attribution will require some exceptionally bright minds that apply mathematical and scientific concepts way beyond most of our comprehension.

The majority of the attribution challenge is that human behavior is extraordinarily complicated and the process of identifying its causes is convoluted. While we have more data than ever, we’re still guessing at intent rather than knowing it. It’s one vital data point we’re without.

Who should own this? Agencies are in a prime position to take on this challenge due to being able to hire resources that can be distributed across a larger client base. Combine this with the cross-category learnings an agency can glean and apply, and agencies have the ability to be in the strongest position they’ve been in for years. Of course, this will come at a price, but marketers may be willing to foot the bill for more accurate attribution that can eliminate significant waste or misspending.